A Degradation Hidden Semi‐Markov Model for Predicting Asset Health and Remaining Useful Life
Date
2025
Authors
Gorjian Jolfaei, N.
Rameezdeen, R.
van der Linden, L.
Gunawan, I.
Chow, C.W.K.
Gorjian, N.
Jin, B.
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Journal article
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Quality and Reliability Engineering International, online, 2025; online(qre.70064):3430-3444
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Abstract
An effective tactical asset management program, incorporating condition and degradation assessments, is essential for asset-intensive organizations to make informed decisions about maintenance and renewal. Since asset degradation is an inherently stochastic phenomenon, models based on stochastic processes are the most suitable approach for accurately predicting it. Asset condition assessment and event data are collected and recorded during inspection and maintenance activities to model equipment degradation. A degradation model that integrates these data associated with assets is highly desirable for predicting the effective and reliable remaining useful life (RUL). This study develops a novel Degradation Hidden Semi-Markov Model that uses both failure and condition data to predict RUL of critical pumps in the wastewater network of a regional town in South Australia
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Copyright 2025 John Wiley & Sons Ltd